Industrial control systems: mathematical and statistical models and techniques
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2012
|
| Schriftenreihe: | Industrial innovation series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | XXI, 360 S. graph. Darst. |
| ISBN: | 9781420075588 |
Internformat
MARC
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| 245 | 1 | 0 | |a Industrial control systems |b mathematical and statistical models and techniques |c Adedeji B. Badiru ; Oye Ibidapo-Obe ; Babatunde J. Ayeni |
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Datensatz im Suchindex
| _version_ | 1804148741061476352 |
|---|---|
| adam_text | Titel: Industrial control systems
Autor: Badiru, Adedeji Bodunde
Jahr: 2012
Contents
Preface................................................................................................................xv
Acknowledgments........................................................................................xvii
Authors.............................................................................................................xix
Chapter 1 Mathematical modeling for product design..........................1
Introduction........................................................................................................1
Literature review................................................................................................3
Memetic algorithm and its application to collaborative design..................5
Proposed framework for collaborative design..........................................5
Pseudocode..................................................................................................6
Parameters.................................................................................................8
Pseudo Code lines....................................................................................8
Forearm crutch design.......................................................................................9
Aluminum union....................................................................................10
Composite tube.......................................................................................10
Design problem formulation.....................................................................10
Design agent for weight decision.........................................................10
Design agent for strength decision......................................................11
Implementation...........................................................................................12
Results and analysis....................................................................................14
Conclusion........................................................................................................15
References..........................................................................................................16
Chapter 2 Dynamic fuzzy systems modeling........................................19
Introduction: Decision support systems and uncertainties.......................19
Decision support systems..........................................................................20
Uncertainty...................................................................................................21
Fuzziness......................................................................................................22
Fuzzy set specifications..............................................................................23
Information type I: Sample of very small size....................................24
Information type II: Linguistic assessment.........................................24
Information type III: Single uncertain measured value....................25
Information type IV: Knowledge based on experience.....................26
Stochastic-fuzzy models............................................................................26
Applications......................................................................................................27
Development model...................................................................................27
The optimization of the fuzzy-stochastic development model...........30
Urban transit systems under uncertainty................................................31
Water resources management under uncertainty...................................32
Energy planning and management under uncertainty....................33
University admissions process in Nigeria: The post-UME
test selection saga........................................................................................33
Conclusions.......................................................................................................35
References..........................................................................................................36
Chapter 3 Stochastic systems modeling..................................................39
Introduction to model types...........................................................................39
Material/iconic models..............................................................................39
Robotic/expert models...............................................................................39
Mathematical models..................................................................................40
General problem formulation...............................................................41
Systems filtering and estimation....................................................................43
Identification................................................................................................43
Correlation techniques....................................................................................45
Advantages..............................................................................................47
System estimation...................................................................................47
Problem formulation..............................................................................47
Nomenclature..........................................................................................47
Maximum likelihood..............................................................................47
Least squares/weighted least squares.................................................48
Bayes estimators.....................................................................................48
Minimum variance.................................................................................49
Partitioned data sets...............................................................................51
Kalmanform...........................................................................................52
Discrete dynamic linear system estimation.............................................52
Observation vector.................................................................................52
Prediction.................................................................................................53
Filtering....................................................................................................53
Smoothing................................................................................................53
Continuous dynamic linear system.....................................................53
Continuous nonlinear estimation.............................................................56
Extended Kalman filter..........................................................................58
Partitional estimation.............................................................................58
Invariant imbedding..............................................................................59
Stochastic approximations/innovations concept...................................60
Model control-Model reduction, model analysis......................................62
Introduction.................................................................................................62
Modal approach for estimation in distributed parameter systems.....65
Modal canonical representation................................................................66
References..........................................................................................................73
Chapter 4 Systems optimization techniques.........................................75
Optimality conditions......................................................................................75
Basic structure of local methods....................................................................81
Descent directions.......................................................................................81
Steepest descent...........................................................................................81
Conjugate gradient......................................................................................82
Newton methods.........................................................................................82
Stochastic central problems............................................................................83
Stochastic approximation...........................................................................85
General stochastic control problem..........................................................85
Intelligent heuristic models............................................................................87
Heuristics......................................................................................................87
Intelligent systems.......................................................................................87
Integrated heuristics...................................................................................88
Genetic algorithms......................................................................................91
Genetic algorithm operators......................................................................92
Applications of heuristics to intelligent systems....................................95
High-performance optimization programming.....................................96
References..........................................................................................................97
Chapter 5 Statistical control techniques.................................................99
Statistical process control................................................................................99
Control charts...................................................................................................99
Types of data for control charts...............................................................100
Variable data..........................................................................................100
Attribute data........................................................................................100
X-bar and range charts.............................................................................100
Data collection strategies..........................................................................101
Subgroup sample size...............................................................................101
Advantages of using small subgroup sample size..........................101
Advantages of using large subgroup sample size...........................101
Frequency of sampling.............................................................................102
Stable process.............................................................................................102
Out-of-control patterns.............................................................................102
Calculation of control limits....................................................................104
Plotting control charts for range and average charts...........................105
Plotting control charts for moving range and individual
control charts..............................................................................................106
Case example: Plotting of control chart.................................................106
Calculations................................................................................................109
Trend analysis.............................................................................................HI
Process capability analysis.............................................................................114
Capable process..........................................................................................115
Capability index.........................................................................................116
Time series analysis and process estimation..............................................120
Correlated observations...........................................................................120
Time series analysis example.......................................................................121
Exponentially weighted moving average...................................................124
Cumulative sum chart...................................................................................125
Engineering feedback control..................................................................127
SPC versus APC.........................................................................................129
Statistical process control....................................................................129
Automatic process control...................................................................129
Criticisms of SPC and APC.................................................................130
Overcompensation, disturbance removal,
and information concealing......................................................................130
Integration of SPC and APC.........................................................................131
Systems approach to process adjustment...................................................131
ARIMA modeling of process data...............................................................132
Model identification and estimation...........................................................134
Minimum variance control...........................................................................135
Process dynamics with disturbance............................................................135
Process modeling and estimation for oil and gas production data........136
Introduction...........................................................................................136
Time series approach-Box and Jenkins methodology....................137
The ARIMA model....................................................................................137
Methodology.........................................................................................138
Decline curve method..........................................................................139
Statistical error analysis............................................................................142
Average relative error..........................................................................142
Average absolute relative error..........................................................142
Forecast root mean square error.........................................................143
Minimum and maximum absolute relative error............................143
Cumulative ratio error.........................................................................143
ARIMA data analysis................................................................................143
Model identification for series WD1..................................................144
Model identification for series Brock......................................................147
Estimation and. diagnostic checking.......................................................149
Comparison of results...............................................................................150
References.........................................................................................................153
Chapter 6 Design of experiment techniques.......................................155
Factorial designs.............................................................................................155
Experimental run.......................................................................................158
One-variable-at-a-time experimentation...............................................158
Experimenting with two factors: 22 design.............................................161
Estimate of the experimental error.........................................................165
Confidence intervals for the effects........................................................166
Factorial design for three factors..................................................................167
Fractional factorial experiments..............................................................171
A 24 factorial design..................................................................................172
Saturated designs............................................................................................176
Central composite designs............................................................................186
Response surface optimization....................................................................186
Applications for moving web processes................................................187
Dual response approach...........................................................................190
Case study of application to moving webs...........................................191
Case application of central composite design............................................193
Analysis of variance..................................................................................195
Response surface optimization...............................................................197
References........................................................................................................200
Chapter 7 Risk analysis and estimation techniques..........................201
Bayesian estimation procedure....................................................................201
Formulation of the oil and gas discovery problem..............................202
Computational procedure.............................................................................202
The k-category case...................................................................................204
Discussion of results.................................................................................205
Parameter estimation for hyperbolic decline curve...................................214
Robustness of decline curves.........................................................................214
Mathematical analysis...................................................................................215
Statistical analysis...........................................................................................216
Parameter estimation.....................................................................................217
Optimization technique................................................................................217
Iterative procedure.........................................................................................219
Residual analysis test.....................................................................................220
Simplified solution to the vector equation.................................................222
Integrating neural networks and statistics for process control...............224
Fundamentals of neural network................................................................225
The input function.........................................................................................225
Transfer functions..........................................................................................226
Statistics and neural networks predictions................................................226
Statistical error analysis.................................................................................226
Integration of statistics and neural networks.............................................227
References........................................................................................................234
Chapter 8 Mathematical modeling and control
of multi-constrained projects...............................................237
Introduction....................................................................................................237
Literature review............................................................................................238
Methodology...................................................................................................240
Representation of resource interdependencies
and multifunctionality..............................................................................240
Modeling of resource characteristics...........................................................242
Resource mapper............................................................................................245
Activity scheduler..........................................................................................247
Model implementation and graphical illustrations..................................255
Notations.........................................................................................................258
References........................................................................................................259
Chapter 9 Online support vector regression with varying
parameters for time-dependent data...................................261
Introduction....................................................................................................261
Modified Gompertz weight function for varying SVR parameters........263
Accurate online SVR with varying parameters.........................................266
Experimental results......................................................................................268
Application to time series data................................................................269
Application to feed-water flow rate data...............................................271
Conclusion......................................................................................................276
References........................................................................................................277
Appendix: Mathematical and engineering formulae...............................279
Index................................................................................................................351
|
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| indexdate | 2024-07-10T00:11:57Z |
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| lccn | 2011039588 |
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| physical | XXI, 360 S. graph. Darst. |
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| spelling | Badiru, Adedeji Bodunde Verfasser aut Industrial control systems mathematical and statistical models and techniques Adedeji B. Badiru ; Oye Ibidapo-Obe ; Babatunde J. Ayeni Boca Raton, Fla. [u.a.] CRC Press 2012 XXI, 360 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Industrial innovation series Mathematisches Modell Process control Statistical methods Process control Mathematical models TECHNOLOGY & ENGINEERING / Industrial Design / General bisacsh TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Electrical bisacsh Prozessüberwachung (DE-588)4133922-8 gnd rswk-swf Regelungstechnik (DE-588)4076594-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Regelungstechnik (DE-588)4076594-5 s Prozessüberwachung (DE-588)4133922-8 s Statistik (DE-588)4056995-0 s DE-604 Ibidapo-Obe, Oye Verfasser aut Ayeni, Babatunde J. Verfasser aut http://www.gbv.de/dms/zbw/670552313.pdf Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024670799&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Badiru, Adedeji Bodunde Ibidapo-Obe, Oye Ayeni, Babatunde J. Industrial control systems mathematical and statistical models and techniques Mathematisches Modell Process control Statistical methods Process control Mathematical models TECHNOLOGY & ENGINEERING / Industrial Design / General bisacsh TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Electrical bisacsh Prozessüberwachung (DE-588)4133922-8 gnd Regelungstechnik (DE-588)4076594-5 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4133922-8 (DE-588)4076594-5 (DE-588)4056995-0 |
| title | Industrial control systems mathematical and statistical models and techniques |
| title_auth | Industrial control systems mathematical and statistical models and techniques |
| title_exact_search | Industrial control systems mathematical and statistical models and techniques |
| title_full | Industrial control systems mathematical and statistical models and techniques Adedeji B. Badiru ; Oye Ibidapo-Obe ; Babatunde J. Ayeni |
| title_fullStr | Industrial control systems mathematical and statistical models and techniques Adedeji B. Badiru ; Oye Ibidapo-Obe ; Babatunde J. Ayeni |
| title_full_unstemmed | Industrial control systems mathematical and statistical models and techniques Adedeji B. Badiru ; Oye Ibidapo-Obe ; Babatunde J. Ayeni |
| title_short | Industrial control systems |
| title_sort | industrial control systems mathematical and statistical models and techniques |
| title_sub | mathematical and statistical models and techniques |
| topic | Mathematisches Modell Process control Statistical methods Process control Mathematical models TECHNOLOGY & ENGINEERING / Industrial Design / General bisacsh TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Electrical bisacsh Prozessüberwachung (DE-588)4133922-8 gnd Regelungstechnik (DE-588)4076594-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Mathematisches Modell Process control Statistical methods Process control Mathematical models TECHNOLOGY & ENGINEERING / Industrial Design / General TECHNOLOGY & ENGINEERING / Engineering (General) TECHNOLOGY & ENGINEERING / Electrical Prozessüberwachung Regelungstechnik Statistik |
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